Onos Health’s mission is simple but ambitious: ensure every healthcare dollar goes toward delivering the highest quality care. Today, 30% of total U.S. healthcare spending is wasted due to ineffective care and administrative burden caused by misalignment between providers and payers.
Onos is addressing this by building the largest AI-driven behavioral healthcare platform. Our models enables payers to make faster, more accurate decisions across their populations. By guiding members to the right care, Onos is channeling more dollars to high-quality care that drives better outcomes while making healthcare more affordable.
Onos is well-funded by some of the best healthcare investors and is working with the nation’s largest health plans. Come join a category-defining company and help reimagine healthcare for the better.
Why Onos?Meaningful impact: Help fix what is fundamentally broken in healthcare
Direct collaboration: Work alongside experienced founders with deep healthcare and data expertise
Culture: Join a high-performing, transparent, and results-oriented team
Ownership: Significant responsibility and autonomy from day one
Opportunity: Play a pivotal role in building a fast-growing, category-defining healthcare AI company
We're seeking an experienced AI/ML engineer who is motivated to meaningfully improve the way healthcare is administered in the United States. You'll be responsible for making the core Onos Health AI extraction and evaluation systems accurate, consistent, and trustworthy enough for health plans to depend on. As an early team member, you'll be expected to wear multiple hats and ensure excellent outcomes for our enterprise customers. This role is a hybrid role based in San Francisco, where you'll be expected to work at our office in person 2-3 times a week.
What you'll be doing at Onos:Develop LLM/NLU systems to process and extract meaningful information from clinical notes and medical documents, classify patients according to level-of-care guidelines, and make accurate recommendations
Own and evolve our LLM evaluation harness, regression gates, and observability to ensure our systems catch accuracy regressions before they reach payers and prove the platform's reliability over time
Extract structured data from visually complex clinical documents, including scanned charts, tables, and graphs using a mix of OCR, multimodal models, and classical ML
Collaborate with backend engineers to integrate AI/ML capabilities seamlessly into the Onos platform
Build and operationalize AI/data pipelines to analyze medical records to streamline clinical assessments and healthcare quality reviews
Benchmark and stress-test LLM systems so evidence extraction and level-of-care classification stay accurate and reliable as criteria, documents, and models change
Develop and optimize a system that ingests complex medical standards of care documents and evaluates provider adherence to guidelines
Design explainable AI solutions that provide transparency into model decisions for healthcare professionals
Tech Stack:
Infrastructure/Systems: AWS (ECS, Bedrock, Cognito, etc.), Docker, Github Actions
Languages/Frameworks: Python, Django, Celery, django-ninja, django-tenants
Database/Storage: PostgreSQL (AWS RDS), S3
Development Tools: Github, Jira, CoderabbitAI, Tusk, Claude
4+ years experience building and deploying applications in production in a backend engineering / data engineering capacity
Relevant experience with developing LLM-based systems for ingesting and evaluating unstructured records for industry-specific use cases and integrating them with user-facing features
Experience with document AI, OCR, or extracting data from visual/scanned content (charts, graphs, tables)
Deep understanding of the limitations of using LLMs and the best practices for using them for reliable, consistent, and accurate outputs
Customer obsessed and motivated to build best-in-class models for behavioral health clinical assessments in the healthcare space
A collaborative team player with a focus on delivering measurable results
Specifically worked with medical records to evaluate whether a patient’s history meets criteria for evaluations or assessments (e.g., claims authorization or other types of evaluations)
Experience wearing multiple hats as a generalist backend engineer
Experience working with data pipelines and Python and related data science/ML libraries
Significant experience working with healthcare data and with HIPAA best practices
Knowledge of modern LLM and ML infrastructure and MLOps best practices
Flexible hybrid arrangement: 2-3 days/week at San Francisco office (Financial District), remote-first culture
Unlimited vacation policy
Paid parental leave
Medical, dental, and vision insurance
Pre-tax commuter benefits
401(k)
Significant equity as an early employee
Direct mentorship from experienced founders
Ground-floor opportunity to help build a team and culture
Regular team events and offsites
Company-provided equipment and home office setup
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Skills Required
- 4+ years experience in backend or data engineering
- Experience developing LLM-based systems
- Experience with document AI and OCR
- Deep understanding of LLM limitations and best practices
- Experience with healthcare data and HIPAA best practices
What We Do
There are too many reasons why Healthcare is unaffordable in the U.S, but one of the largest is waste. Up to 40% of healthcare spend is lost on inefficiencies, administrative costs, and ineffective care practices. We’re building a system that unites providers and payers with one goal: delivering the highest quality care while eliminating waste.
